system

Abstract

The system according to this embodiment aims to automatically grasp the progress of a project and efficiently manage and execute tasks. [Solution] The system according to the embodiment comprises a reception unit, a data collection unit, a management unit, and an execution unit. The reception unit receives input of project information. The data collection unit grasps the progress of the project based on the information received by the reception unit. The management unit manages tasks based on the progress grasped by the data collection unit. The execution unit executes the tasks managed by the management unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that in grasping the progress of a project and task management, a lot of manual work and judgment are required in many parts, making efficient management difficult.

[0005] The system according to the embodiment aims to automatically grasp the progress of a project and efficiently manage and execute tasks.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a data collection unit, a management unit, and an execution unit. The reception unit receives input of project information. The data collection unit grasps the progress of the project based on the information received by the reception unit. The management unit manages tasks based on the progress grasped by the data collection unit. The execution unit executes the tasks managed by the management unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically grasp the progress of a project and efficiently manage and execute tasks. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​project management system according to an embodiment of the present invention is an AI agent for successfully managing numerous projects, both large and small, currently underway within a company. While many AI project management systems exist to streamline the work of project managers through schedule and task management, much of this still requires human intervention and judgment. The AI ​​project management system, by requiring only minimal input of necessary information, automatically grasps project progress, manages and executes tasks, and integrates with various project-related systems and business data. For example, the AI ​​project management system automatically manages project schedules and milestones. It grasps task progress and executes tasks as needed. It automatically schedules project-related meetings. It automatically sends project-related emails. It automatically creates daily reports on project progress. It manages project progress in real time. It automatically analyzes project risks and proposes countermeasures. As a result, the AI ​​project management system is expected to significantly reduce project management workload. Because the AI ​​agent takes over tasks previously handled by humans, project management workload can be drastically reduced. Furthermore, it is particularly effective for projects following the same process, and even more so when multiple projects, such as system development or software development, are being managed using the same process, as it allows for comparisons with past projects. As a result, the AI ​​project management system can automatically grasp the progress of projects and manage and execute tasks.

[0029] The AI ​​project management system according to this embodiment comprises a reception unit, a data collection unit, a management unit, and an execution unit. The reception unit receives input of project information. Project information includes, but is not limited to, the project name, start date, end date, person in charge, and progress status. The reception unit provides, for example, an interface for inputting basic information such as the project name and start date. The reception unit also provides an interface for inputting the project progress status. The data collection unit grasps the project progress status based on the information received by the reception unit. Progress status includes, for example, the task completion rate, adherence to deadlines, and resource usage status. The data collection unit monitors the project progress status in real time and grasps the progress status. The data collection unit can also display the progress status as a graph or chart. The management unit manages tasks based on the progress status grasped by the data collection unit. Tasks include, for example, the task type, priority, person in charge, and deadline, but is not limited to these. The management unit determines the priority of tasks and allocates resources. Furthermore, the management unit can track the progress of tasks and reassign tasks as needed. The execution unit executes the tasks managed by the management unit. The execution unit, for example, provides the task execution procedure and monitors the task execution status. The execution unit can also record the results of task execution and reflect them in the next task. As a result, the AI ​​project management system according to this embodiment improves the efficiency of project management by automating everything from inputting project information to understanding progress, managing tasks, and executing them.

[0030] The reception desk accepts project information. Project information includes, but is not limited to, the project name, start date, end date, assigned person, and progress status. The reception desk provides an interface for entering basic information such as the project name and start date. It also provides an interface for entering project progress status. Specifically, the reception desk features a user-friendly interface designed for easy entry of basic project information. For example, it provides text boxes and a calendar widget for entering the project name, start date, and end date. Furthermore, it offers dropdown menus and checkboxes for selecting assigned persons and updating progress status. This allows users to intuitively enter information and reduces input errors. The reception desk also has a function to automatically validate the entered information and display warnings if there is missing or inaccurate information. For example, if the end date is set before the start date, or if an assigned person has not been selected, it displays an error message prompting the user to correct it. This allows the reception desk to collect accurate and complete project information. Furthermore, the reception desk saves the entered information to a database, making it accessible to other departments. This allows project information to be centrally managed and quickly accessed when needed.

[0031] The tracking unit understands the project's progress based on information received by the reception unit. Progress includes, but is not limited to, task completion rates, deadline compliance, and resource usage. The tracking unit monitors project progress in real time and understands its status. It can also display progress as graphs and charts. Specifically, the tracking unit provides a dashboard for visually displaying project progress. This dashboard includes bar graphs showing task completion rates, Gantt charts showing deadline compliance, and pie charts showing resource usage. This allows project managers and team members to grasp project progress at a glance. Furthermore, the tracking unit updates progress data in real time, providing the latest information. For example, when a task is completed or resource usage changes, it is immediately reflected on the dashboard. This ensures that project progress is always up-to-date. The tracking unit also includes an alert function regarding progress. For example, it displays alerts to notify users if tasks are nearing deadlines or resources are insufficient. This allows for the early detection of project delays and resource shortages, enabling appropriate countermeasures to be taken.

[0032] The management department manages tasks based on the progress status tracked by the monitoring department. Tasks include, but are not limited to, task type, priority, assigned person, and deadline. The management department, for example, determines task priorities and allocates resources. It can also track task progress and reallocate tasks as needed. Specifically, the management department has an algorithm that automatically determines task priorities based on project progress. This algorithm calculates the optimal priority by considering factors such as task importance, deadlines, and resource utilization. This allows project managers to efficiently manage tasks and allocate resources optimally. The management department also has the ability to track task progress in real time and reallocate tasks as needed. For example, if a task is delayed or an assigned person is under-resourced, the management department can reallocate the task to another person to maintain smooth project progress. Furthermore, the management department provides tools for visualizing task progress. These include, for example, Gantt charts showing task progress and heatmaps showing resource usage. This allows project managers to grasp the progress of tasks at a glance and manage them appropriately.

[0033] The execution unit carries out tasks managed by the management unit. For example, the execution unit provides task execution procedures and monitors task execution status. It can also record task execution results and incorporate them into subsequent tasks. Specifically, the execution unit provides guidelines and checklists to offer detailed task execution procedures. This allows team members to accurately understand task execution procedures and complete tasks efficiently. The execution unit also has the functionality to monitor task execution status in real time and understand progress. For example, it displays bar graphs showing task progress and lists of tasks currently in progress. This allows project managers and team members to grasp task execution status at a glance. Furthermore, the execution unit has the functionality to record task execution results and incorporate them into subsequent tasks. For example, upon task completion, it can record execution results, problems, and areas for improvement, and utilize this information for subsequent tasks. This helps maintain smooth project progress and efficiently complete tasks. Additionally, the execution unit can collect feedback on task execution status and use it to improve the overall system. For example, based on feedback from those in charge, execution procedures and guidelines can be reviewed to achieve more efficient task execution.

[0034] The meeting scheduling unit can automatically schedule meetings. For example, it can automatically schedule meetings related to a project. These meetings include, but are not limited to, regular meetings, project meetings, and online meetings. The meeting scheduling unit can automatically schedule meetings based on the project's progress. It can also automatically invite meeting participants. For example, it can monitor project progress and automatically schedule necessary meetings. It can automatically adjust meeting schedules and notify participants. It can automatically set meeting agendas and share them with participants. By automating meeting scheduling, the efficiency of project management is improved.

[0035] The email sending unit can automatically send emails related to a project. For example, the email sending unit can automatically send emails based on the project's progress. These emails include, but are not limited to, notification emails, reminder emails, and report emails. For example, the email sending unit can monitor the project's progress and automatically send necessary emails. The email sending unit automatically generates the email content and sends it to the recipient. The email sending unit automatically adjusts the timing of email sending and notifies the recipient. This automates email sending, thereby improving the efficiency of project management.

[0036] The daily report creation department can automatically generate daily reports on project progress. For example, the daily report creation department can automatically create daily reports on project progress. These daily reports may include, but are not limited to, daily progress reports, problem reports, and the next day's schedule. For example, the daily report creation department can grasp the project progress and automatically create daily reports. The daily report creation department automatically generates the content of the daily reports and shares them with relevant parties. The daily report creation department automatically adjusts the timing of daily report creation and notifies relevant parties. By automating the creation of daily reports, the efficiency of project management is improved.

[0037] The risk analysis department can automatically analyze project risks and propose countermeasures. For example, the risk analysis department can automatically analyze project risks and propose countermeasures. Risks include, but are not limited to, technical risks, schedule risks, and cost risks. For example, the risk analysis department can grasp the progress of a project and automatically analyze risks. The risk analysis department can automatically evaluate the nature of the risks and propose countermeasures. The risk analysis department can automatically calculate the probability of risk occurrence and its impact and formulate countermeasures. By automating risk analysis and countermeasure proposals, the efficiency of project management is improved.

[0038] The management department can prioritize tasks and allocate resources. For example, the management department can determine task priorities and allocate resources. The management department can determine priorities based on the importance and urgency of tasks. The management department can allocate resources based on their availability and skills. The management department can also track task progress and reallocate tasks as needed. This improves the efficiency of project management by automating task prioritization and resource allocation.

[0039] The reception desk can analyze past project information and select the optimal input method. For example, the reception desk can suggest the most efficient input method based on past project information. Based on past project information, the reception desk prioritizes suggesting input methods preferred by the user. The reception desk analyzes past project information and selects a method that minimizes input errors. As a result, input efficiency is improved by selecting the optimal input method based on past project information.

[0040] The reception desk can filter project information input based on the user's current work situation and areas of interest. For example, the reception desk considers the user's current work situation and inputs only highly relevant information. The reception desk prioritizes inputting necessary information based on the user's areas of interest. The reception desk filters out unnecessary information to reduce the user's workload. This improves input efficiency by filtering information based on the user's work situation and areas of interest.

[0041] The reception desk can prioritize inputting highly relevant information when entering project information, taking into account the user's geographical location. For example, the reception desk prioritizes inputting relevant project information based on the user's current location. The reception desk prioritizes inputting region-specific information, taking into account the user's geographical location. The reception desk suggests the most suitable project information based on the user's location. In this way, by considering the user's geographical location, highly relevant information can be prioritized for input.

[0042] The reception desk can analyze users' social media activity and input relevant information when entering project information. For example, the reception desk analyzes users' social media activity and inputs relevant project information. The reception desk prioritizes inputting necessary information based on users' interests on social media. The reception desk filters project information by referring to the content of users' social media posts. This allows for efficient input of relevant information by analyzing users' social media activity.

[0043] The progress tracking unit can predict current progress by referring to past project data when monitoring progress. For example, the tracking unit predicts current progress based on past project data. The tracking unit predicts delays in progress by referring to past project data. The tracking unit predicts the speed of progress by analyzing past project data. As a result, monitoring progress becomes more efficient by predicting current progress based on past project data.

[0044] The progress tracking unit can apply different tracking methods to each project category when tracking progress. For example, the tracking unit applies the most suitable progress tracking method according to the project category. The tracking unit adjusts the progress reporting method for each project category. The tracking unit selects the progress tracking method based on the project category. This makes progress tracking more efficient by applying the most suitable tracking method according to the project category.

[0045] The progress tracking unit can prioritize progress based on project submission deadlines when monitoring progress. For example, the unit prioritizes progress based on project submission deadlines. The unit prioritizes monitoring the progress of projects with upcoming submission deadlines. The unit adjusts the frequency of progress reports according to the submission deadlines. This streamlines progress tracking by prioritizing progress based on project submission deadlines.

[0046] The tracking unit can improve the accuracy of its progress assessment by referring to project-related literature. For example, the tracking unit can improve the accuracy of its progress assessment by referring to project-related literature. The tracking unit predicts delays in progress based on the related literature. The tracking unit predicts the speed of progress by referring to related literature. In this way, the accuracy of the progress assessment is improved by referring to project-related literature.

[0047] The management department can select the optimal management method by referring to past task data when managing tasks. For example, the management department selects the optimal management method based on past task data. The management department refers to past task data to determine task priorities. The management department analyzes past task data and proposes efficient management methods. As a result, task management becomes more efficient by selecting the optimal management method based on past task data.

[0048] The management department can apply different management methods to each project category when managing tasks. For example, the management department applies the most suitable management method depending on the project category. The management department determines the priority of tasks for each project category. The management department selects the task management method based on the project category. This makes task management more efficient by applying the most suitable management method according to the project category.

[0049] The management department can prioritize tasks based on project submission deadlines during task management. For example, the management department prioritizes tasks based on project submission deadlines. The management department manages tasks with approaching submission deadlines first. The management department adjusts task management methods according to submission deadlines. This streamlines task management by prioritizing tasks based on project submission deadlines.

[0050] The management department can improve the accuracy of task management by referring to project-related literature. For example, the management department can improve the accuracy of task management by referring to project-related literature. Based on the relevant literature, the management department can determine task priorities. The management department can also propose efficient task management methods by referring to the relevant literature. Thus, by referring to project-related literature, the accuracy of task management is improved.

[0051] The execution unit can select the optimal execution method by referring to past execution data when executing a task. For example, the execution unit selects the optimal execution method based on past execution data. The execution unit refers to past execution data and selects a method to improve execution efficiency. The execution unit analyzes past execution data and selects an execution method that minimizes errors. As a result, task execution is made more efficient by selecting the optimal execution method based on past execution data.

[0052] The execution unit can apply different execution methods to each project category when executing tasks. For example, the execution unit applies the optimal execution method according to the project category. The execution unit determines the execution priority for each project category. The execution unit selects an execution method based on the project category. This makes task execution more efficient by applying the optimal execution method according to the project category.

[0053] The execution unit can determine the execution priority based on the project submission deadline when executing tasks. For example, the execution unit determines the execution priority based on the project submission deadline. The execution unit prioritizes tasks with upcoming submission deadlines. The execution unit adjusts the execution method according to the submission deadline. This makes task execution more efficient by determining the execution priority based on the project submission deadline.

[0054] The execution unit can improve the accuracy of task execution by referring to relevant project documentation during task execution. For example, the execution unit can refer to relevant project documentation to improve execution accuracy. Based on the relevant documentation, the execution unit determines execution priorities. The execution unit also refers to relevant documentation to suggest efficient execution methods. Thus, by referring to relevant project documentation, the accuracy of task execution is improved.

[0055] The meeting scheduling unit can select the optimal scheduling method by referring to past meeting data when scheduling a meeting. For example, the meeting scheduling unit selects the optimal scheduling method based on past meeting data. The meeting scheduling unit refers to past meeting data and selects a method to improve meeting efficiency. The meeting scheduling unit analyzes past meeting data and selects a scheduling method that minimizes errors. As a result, meeting scheduling becomes more efficient by selecting the optimal scheduling method based on past meeting data.

[0056] The meeting scheduling unit can determine meeting priorities based on project submission deadlines when scheduling meetings. For example, the meeting scheduling unit will determine meeting priorities based on project submission deadlines. The meeting scheduling unit will prioritize meetings with upcoming submission deadlines. The meeting scheduling unit will adjust the meeting scheduling method according to the submission deadlines. This streamlines meeting scheduling by determining meeting priorities based on project submission deadlines.

[0057] The email sending unit can select the optimal sending method by referring to past email data when sending an email. For example, the email sending unit selects the optimal sending method based on past email data. The email sending unit refers to past email data and selects a method to improve email efficiency. The email sending unit analyzes past email data and selects a sending method that minimizes errors. As a result, email sending becomes more efficient by selecting the optimal sending method based on past email data.

[0058] The email sending unit can prioritize emails based on project submission deadlines. For example, the email sending unit prioritizes emails based on project submission deadlines. The email sending unit sends emails with upcoming submission deadlines first. The email sending unit adjusts the email sending method according to the submission deadline. This streamlines email sending by prioritizing emails based on project submission deadlines.

[0059] The daily report creation department can select the optimal creation method by referring to past daily report data when creating daily reports. For example, the daily report creation department selects the optimal creation method based on past daily report data. The daily report creation department refers to past daily report data and selects a method to improve the efficiency of daily reports. The daily report creation department analyzes past daily report data and selects a creation method that minimizes errors. As a result, daily report creation becomes more efficient by selecting the optimal creation method based on past daily report data.

[0060] The daily report creation department can prioritize daily reports based on project submission deadlines. For example, the department prioritizes daily reports based on project submission deadlines. The department prioritizes creating daily reports with upcoming submission deadlines. The department adjusts the daily report creation method according to the submission deadline. This streamlines daily report creation by prioritizing daily reports based on project submission deadlines.

[0061] The Risk Analysis Department can select the optimal analysis method by referring to past risk data during risk analysis. For example, the Risk Analysis Department selects the optimal analysis method based on past risk data. The Risk Analysis Department refers to past risk data and determines the priority of risks. The Risk Analysis Department analyzes past risk data and proposes an efficient analysis method. In this way, risk analysis becomes more efficient by selecting the optimal analysis method based on past risk data.

[0062] The Risk Analysis Department can apply different analytical methods to each project category during risk analysis. For example, the Risk Analysis Department applies the most suitable analytical method according to the project category. The Risk Analysis Department determines the priority of risks for each project category. The Risk Analysis Department selects an analytical method based on the project category. This makes risk analysis more efficient by applying the most suitable analytical method according to the project category.

[0063] The risk analysis department can prioritize risks based on the project submission deadline during risk analysis. For example, the risk analysis department prioritizes risks based on the project submission deadline. The risk analysis department prioritizes analyzing risks with upcoming submission deadlines. The risk analysis department adjusts its risk analysis methods according to the submission deadline. This streamlines risk analysis by prioritizing risks based on the project submission deadline.

[0064] The risk analysis department can improve the accuracy of its risk analysis by referring to relevant project literature. For example, the risk analysis department can improve the accuracy of its risk analysis by referring to relevant project literature. Based on the relevant literature, the risk analysis department can determine the priority of risks. The risk analysis department can propose efficient risk analysis methods by referring to relevant literature. Thus, by referring to relevant project literature, the accuracy of the risk analysis is improved.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] AI project management systems can also include features to evaluate the individual work performance of project members in order to understand project progress. For example, the monitoring unit can monitor the speed and quality of each member's task completion and collect performance data. The management unit can then reallocate tasks and optimize resources based on the collected performance data. Furthermore, the execution unit can use the performance data to automatically assign tasks according to the skills of the members. This allows for a more accurate understanding of project progress and more efficient task management.

[0067] AI project management systems can also provide dashboards that visualize project progress to help users understand the project's status. For example, the monitoring unit can display progress in real time as graphs and charts. The management unit can adjust task priorities and resource allocations through the dashboard. The execution unit can also use the dashboard to monitor task progress and reallocate tasks as needed. This makes it easier to visually understand project progress and enables efficient project management.

[0068] AI project management systems can also include features to analyze the communication history of project members in order to understand the progress of a project. For example, the monitoring unit can analyze the content of emails and chats between members to understand the frequency and content of communication. The management unit can identify the progress and problems of tasks based on the communication history and take necessary measures. Furthermore, the execution unit can use the communication history to strengthen collaboration among members and ensure the smooth execution of tasks. This allows for a more detailed understanding of project progress and enables more efficient project management.

[0069] AI project management systems can also include feedback functions to improve the productivity of project members in order to understand project progress. For example, the monitoring unit can monitor the speed and quality of members' task completion and provide feedback. The management unit can then reallocate tasks and optimize resources based on the feedback. Furthermore, the execution unit can use the feedback to automatically assign tasks according to members' skills. This allows for a more accurate understanding of project progress and more efficient task management.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The reception desk accepts project information. This project information includes the project name, start date, end date, assigned person, and progress status. The reception desk provides an interface for entering this information. Step 2: The monitoring unit grasps the project's progress based on the information received by the reception unit. This progress includes task completion rates, deadline adherence, and resource usage. The monitoring unit can monitor project progress in real time and display the progress as graphs or charts. Step 3: The management department manages tasks based on the progress tracked by the tracking department. Tasks include task type, priority, assignee, and deadline. The management department determines task priorities and allocates resources. They can also track task progress and reallocate tasks as needed. Step 4: The execution unit executes tasks managed by the management unit. The execution unit provides the task execution procedures and monitors the task execution status. It can also record the task execution results and use them to improve subsequent tasks.

[0072] (Example of form 2) The AI ​​project management system according to an embodiment of the present invention is an AI agent for successfully managing numerous projects, both large and small, currently underway within a company. While many AI project management systems exist to streamline the work of project managers through schedule and task management, much of this still requires human intervention and judgment. The AI ​​project management system, by requiring only minimal input of necessary information, automatically grasps project progress, manages and executes tasks, and integrates with various project-related systems and business data. For example, the AI ​​project management system automatically manages project schedules and milestones. It grasps task progress and executes tasks as needed. It automatically schedules project-related meetings. It automatically sends project-related emails. It automatically creates daily reports on project progress. It manages project progress in real time. It automatically analyzes project risks and proposes countermeasures. As a result, the AI ​​project management system is expected to significantly reduce project management workload. Because the AI ​​agent takes over tasks previously handled by humans, project management workload can be drastically reduced. Furthermore, it is particularly effective for projects following the same process, and even more so when multiple projects, such as system development or software development, are being managed using the same process, as it allows for comparisons with past projects. As a result, the AI ​​project management system can automatically grasp the progress of projects and manage and execute tasks.

[0073] The AI ​​project management system according to this embodiment comprises a reception unit, a data collection unit, a management unit, and an execution unit. The reception unit receives input of project information. Project information includes, but is not limited to, the project name, start date, end date, person in charge, and progress status. The reception unit provides, for example, an interface for inputting basic information such as the project name and start date. The reception unit also provides an interface for inputting the project progress status. The data collection unit grasps the project progress status based on the information received by the reception unit. Progress status includes, for example, the task completion rate, adherence to deadlines, and resource usage status. The data collection unit monitors the project progress status in real time and grasps the progress status. The data collection unit can also display the progress status as a graph or chart. The management unit manages tasks based on the progress status grasped by the data collection unit. Tasks include, for example, the task type, priority, person in charge, and deadline, but is not limited to these. The management unit determines the priority of tasks and allocates resources. Furthermore, the management unit can track the progress of tasks and reassign tasks as needed. The execution unit executes the tasks managed by the management unit. The execution unit, for example, provides the task execution procedure and monitors the task execution status. The execution unit can also record the results of task execution and reflect them in the next task. As a result, the AI ​​project management system according to this embodiment improves the efficiency of project management by automating everything from inputting project information to understanding progress, managing tasks, and executing them.

[0074] The reception desk accepts project information. Project information includes, but is not limited to, the project name, start date, end date, assigned person, and progress status. The reception desk provides an interface for entering basic information such as the project name and start date. It also provides an interface for entering project progress status. Specifically, the reception desk features a user-friendly interface designed for easy entry of basic project information. For example, it provides text boxes and a calendar widget for entering the project name, start date, and end date. Furthermore, it offers dropdown menus and checkboxes for selecting assigned persons and updating progress status. This allows users to intuitively enter information and reduces input errors. The reception desk also has a function to automatically validate the entered information and display warnings if there is missing or inaccurate information. For example, if the end date is set before the start date, or if an assigned person has not been selected, it displays an error message prompting the user to correct it. This allows the reception desk to collect accurate and complete project information. Furthermore, the reception desk saves the entered information to a database, making it accessible to other departments. This allows project information to be centrally managed and quickly accessed when needed.

[0075] The tracking unit understands the project's progress based on information received by the reception unit. Progress includes, but is not limited to, task completion rates, deadline compliance, and resource usage. The tracking unit monitors project progress in real time and understands its status. It can also display progress as graphs and charts. Specifically, the tracking unit provides a dashboard for visually displaying project progress. This dashboard includes bar graphs showing task completion rates, Gantt charts showing deadline compliance, and pie charts showing resource usage. This allows project managers and team members to grasp project progress at a glance. Furthermore, the tracking unit updates progress data in real time, providing the latest information. For example, when a task is completed or resource usage changes, it is immediately reflected on the dashboard. This ensures that project progress is always up-to-date. The tracking unit also includes an alert function regarding progress. For example, it displays alerts to notify users if tasks are nearing deadlines or resources are insufficient. This allows for the early detection of project delays and resource shortages, enabling appropriate countermeasures to be taken.

[0076] The management department manages tasks based on the progress status tracked by the monitoring department. Tasks include, but are not limited to, task type, priority, assigned person, and deadline. The management department, for example, determines task priorities and allocates resources. It can also track task progress and reallocate tasks as needed. Specifically, the management department has an algorithm that automatically determines task priorities based on project progress. This algorithm calculates the optimal priority by considering factors such as task importance, deadlines, and resource utilization. This allows project managers to efficiently manage tasks and allocate resources optimally. The management department also has the ability to track task progress in real time and reallocate tasks as needed. For example, if a task is delayed or an assigned person is under-resourced, the management department can reallocate the task to another person to maintain smooth project progress. Furthermore, the management department provides tools for visualizing task progress. These include, for example, Gantt charts showing task progress and heatmaps showing resource usage. This allows project managers to grasp the progress of tasks at a glance and manage them appropriately.

[0077] The execution unit carries out tasks managed by the management unit. For example, the execution unit provides task execution procedures and monitors task execution status. It can also record task execution results and incorporate them into subsequent tasks. Specifically, the execution unit provides guidelines and checklists to offer detailed task execution procedures. This allows team members to accurately understand task execution procedures and complete tasks efficiently. The execution unit also has the functionality to monitor task execution status in real time and understand progress. For example, it displays bar graphs showing task progress and lists of tasks currently in progress. This allows project managers and team members to grasp task execution status at a glance. Furthermore, the execution unit has the functionality to record task execution results and incorporate them into subsequent tasks. For example, upon task completion, it can record execution results, problems, and areas for improvement, and utilize this information for subsequent tasks. This helps maintain smooth project progress and efficiently complete tasks. Additionally, the execution unit can collect feedback on task execution status and use it to improve the overall system. For example, based on feedback from those in charge, execution procedures and guidelines can be reviewed to achieve more efficient task execution.

[0078] The meeting scheduling unit can automatically schedule meetings. For example, it can automatically schedule meetings related to a project. These meetings include, but are not limited to, regular meetings, project meetings, and online meetings. The meeting scheduling unit can automatically schedule meetings based on the project's progress. It can also automatically invite meeting participants. For example, it can monitor project progress and automatically schedule necessary meetings. It can automatically adjust meeting schedules and notify participants. It can automatically set meeting agendas and share them with participants. By automating meeting scheduling, the efficiency of project management is improved.

[0079] The email sending unit can automatically send emails related to a project. For example, the email sending unit can automatically send emails based on the project's progress. These emails include, but are not limited to, notification emails, reminder emails, and report emails. For example, the email sending unit can monitor the project's progress and automatically send necessary emails. The email sending unit automatically generates the email content and sends it to the recipient. The email sending unit automatically adjusts the timing of email sending and notifies the recipient. This automates email sending, thereby improving the efficiency of project management.

[0080] The daily report creation department can automatically generate daily reports on project progress. For example, the daily report creation department can automatically create daily reports on project progress. These daily reports may include, but are not limited to, daily progress reports, problem reports, and the next day's schedule. For example, the daily report creation department can grasp the project progress and automatically create daily reports. The daily report creation department automatically generates the content of the daily reports and shares them with relevant parties. The daily report creation department automatically adjusts the timing of daily report creation and notifies relevant parties. By automating the creation of daily reports, the efficiency of project management is improved.

[0081] The risk analysis department can automatically analyze project risks and propose countermeasures. For example, the risk analysis department can automatically analyze project risks and propose countermeasures. Risks include, but are not limited to, technical risks, schedule risks, and cost risks. For example, the risk analysis department can grasp the progress of a project and automatically analyze risks. The risk analysis department can automatically evaluate the nature of the risks and propose countermeasures. The risk analysis department can automatically calculate the probability of risk occurrence and its impact and formulate countermeasures. By automating risk analysis and countermeasure proposals, the efficiency of project management is improved.

[0082] The management department can prioritize tasks and allocate resources. For example, the management department can determine task priorities and allocate resources. The management department can determine priorities based on the importance and urgency of tasks. The management department can allocate resources based on their availability and skills. The management department can also track task progress and reallocate tasks as needed. This improves the efficiency of project management by automating task prioritization and resource allocation.

[0083] The reception desk can estimate the user's emotions and adjust the timing of project information input based on the estimated emotions. For example, if the user is stressed, the reception desk will delay the input timing to provide time for relaxation. If the user is relaxed, the reception desk will speed up the input timing to efficiently collect information. If the user is in a hurry, the reception desk will immediately set the input timing to collect information quickly. In this way, project information input is made more efficient by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The reception desk can analyze past project information and select the optimal input method. For example, the reception desk can suggest the most efficient input method based on past project information. Based on past project information, the reception desk prioritizes suggesting input methods preferred by the user. The reception desk analyzes past project information and selects a method that minimizes input errors. As a result, input efficiency is improved by selecting the optimal input method based on past project information.

[0085] The reception desk can filter project information input based on the user's current work situation and areas of interest. For example, the reception desk considers the user's current work situation and inputs only highly relevant information. The reception desk prioritizes inputting necessary information based on the user's areas of interest. The reception desk filters out unnecessary information to reduce the user's workload. This improves input efficiency by filtering information based on the user's work situation and areas of interest.

[0086] The reception desk can estimate the user's emotions and prioritize the project information to be entered based on those emotions. For example, if the user is stressed, the reception desk will postpone less important information. If the user is relaxed, the reception desk will prioritize the input of more important information. If the user is in a hurry, the reception desk will immediately input the most important information. This improves input efficiency by prioritizing information according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The reception desk can prioritize inputting highly relevant information when entering project information, taking into account the user's geographical location. For example, the reception desk prioritizes inputting relevant project information based on the user's current location. The reception desk prioritizes inputting region-specific information, taking into account the user's geographical location. The reception desk suggests the most suitable project information based on the user's location. In this way, by considering the user's geographical location, highly relevant information can be prioritized for input.

[0088] The reception desk can analyze users' social media activity and input relevant information when entering project information. For example, the reception desk analyzes users' social media activity and inputs relevant project information. The reception desk prioritizes inputting necessary information based on users' interests on social media. The reception desk filters project information by referring to the content of users' social media posts. This allows for efficient input of relevant information by analyzing users' social media activity.

[0089] The understanding unit can estimate the user's emotions and adjust the method of tracking progress based on the estimated emotions. For example, if the user is stressed, the understanding unit will provide a concise progress report. If the user is relaxed, the understanding unit will provide a detailed progress report. If the user is in a hurry, the understanding unit will provide a concise progress report. This makes tracking progress more efficient by adjusting the method of tracking progress according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The progress tracking unit can predict current progress by referring to past project data when monitoring progress. For example, the tracking unit predicts current progress based on past project data. The tracking unit predicts delays in progress by referring to past project data. The tracking unit predicts the speed of progress by analyzing past project data. As a result, monitoring progress becomes more efficient by predicting current progress based on past project data.

[0091] The progress tracking unit can apply different tracking methods to each project category when tracking progress. For example, the tracking unit applies the most suitable progress tracking method according to the project category. The tracking unit adjusts the progress reporting method for each project category. The tracking unit selects the progress tracking method based on the project category. This makes progress tracking more efficient by applying the most suitable tracking method according to the project category.

[0092] The understanding unit can estimate the user's emotions and adjust the display method of the progress status based on the estimated user emotions. For example, if the user is tense, the understanding unit provides a simple and highly visible display method. If the user is relaxed, the understanding unit provides a display method that includes detailed information. If the user is in a hurry, the understanding unit provides a display method that gets straight to the point. In this way, the display of progress status becomes more efficient by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The progress tracking unit can prioritize progress based on project submission deadlines when monitoring progress. For example, the unit prioritizes progress based on project submission deadlines. The unit prioritizes monitoring the progress of projects with upcoming submission deadlines. The unit adjusts the frequency of progress reports according to the submission deadlines. This streamlines progress tracking by prioritizing progress based on project submission deadlines.

[0094] The tracking unit can improve the accuracy of its progress assessment by referring to project-related literature. For example, the tracking unit can improve the accuracy of its progress assessment by referring to project-related literature. The tracking unit predicts delays in progress based on the related literature. The tracking unit predicts the speed of progress by referring to related literature. In this way, the accuracy of the progress assessment is improved by referring to project-related literature.

[0095] The management unit can estimate the user's emotions and adjust task management methods based on the estimated emotions. For example, if the user is stressed, the management unit will simplify task management. If the user is relaxed, the management unit will provide detailed task management methods. If the user is in a hurry, the management unit will provide methods for managing tasks quickly. This makes task management more efficient by adjusting task management methods according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The management department can select the optimal management method by referring to past task data when managing tasks. For example, the management department selects the optimal management method based on past task data. The management department refers to past task data to determine task priorities. The management department analyzes past task data and proposes efficient management methods. As a result, task management becomes more efficient by selecting the optimal management method based on past task data.

[0097] The management department can apply different management methods to each project category when managing tasks. For example, the management department applies the most suitable management method depending on the project category. The management department determines the priority of tasks for each project category. The management department selects the task management method based on the project category. This makes task management more efficient by applying the most suitable management method according to the project category.

[0098] The management department can estimate the user's emotions and prioritize tasks based on those emotions. For example, if the user is stressed, the management department will postpone less important tasks. If the user is relaxed, the management department will prioritize managing high-priority tasks. If the user is in a hurry, the management department will immediately manage the most important tasks. This streamlines task management by prioritizing tasks according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The management department can prioritize tasks based on project submission deadlines during task management. For example, the management department prioritizes tasks based on project submission deadlines. The management department manages tasks with approaching submission deadlines first. The management department adjusts task management methods according to submission deadlines. This streamlines task management by prioritizing tasks based on project submission deadlines.

[0100] The management department can improve the accuracy of task management by referring to project-related literature. For example, the management department can improve the accuracy of task management by referring to project-related literature. Based on the relevant literature, the management department can determine task priorities. The management department can also propose efficient task management methods by referring to the relevant literature. Thus, by referring to project-related literature, the accuracy of task management is improved.

[0101] The execution unit can estimate the user's emotions and adjust how tasks are executed based on those estimated emotions. For example, if the user is stressed, the execution unit will start with simple tasks. If the user is relaxed, the execution unit will execute complex tasks. If the user is in a hurry, the execution unit will prioritize tasks that can be completed quickly. This makes task execution more efficient by adjusting how tasks are executed according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The execution unit can select the optimal execution method by referring to past execution data when executing a task. For example, the execution unit selects the optimal execution method based on past execution data. The execution unit refers to past execution data and selects a method to improve execution efficiency. The execution unit analyzes past execution data and selects an execution method that minimizes errors. As a result, task execution is made more efficient by selecting the optimal execution method based on past execution data.

[0103] The execution unit can apply different execution methods to each project category when executing tasks. For example, the execution unit applies the optimal execution method according to the project category. The execution unit determines the execution priority for each project category. The execution unit selects an execution method based on the project category. This makes task execution more efficient by applying the optimal execution method according to the project category.

[0104] The execution unit can estimate the user's emotions and determine the order in which tasks are executed based on the estimated emotions. For example, if the user is stressed, the execution unit will start with simple tasks. If the user is relaxed, the execution unit will execute complex tasks. If the user is in a hurry, the execution unit will prioritize tasks that can be completed quickly. This makes task execution more efficient by determining the order of tasks according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The execution unit can determine the execution priority based on the project submission deadline when executing tasks. For example, the execution unit determines the execution priority based on the project submission deadline. The execution unit prioritizes tasks with upcoming submission deadlines. The execution unit adjusts the execution method according to the submission deadline. This makes task execution more efficient by determining the execution priority based on the project submission deadline.

[0106] The execution unit can improve the accuracy of task execution by referring to relevant project documentation during task execution. For example, the execution unit can refer to relevant project documentation to improve execution accuracy. Based on the relevant documentation, the execution unit determines execution priorities. The execution unit also refers to relevant documentation to suggest efficient execution methods. Thus, by referring to relevant project documentation, the accuracy of task execution is improved.

[0107] The meeting scheduling unit can estimate the user's emotions and adjust the meeting scheduling method based on the estimated emotions. For example, if the user is stressed, the meeting scheduling unit will schedule a short and efficient meeting. If the user is relaxed, the meeting scheduling unit will schedule a meeting with a detailed agenda. If the user is in a hurry, the meeting scheduling unit will schedule a meeting quickly. In this way, the meeting scheduling process is made more efficient by adjusting the meeting scheduling method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The meeting scheduling unit can select the optimal scheduling method by referring to past meeting data when scheduling a meeting. For example, the meeting scheduling unit selects the optimal scheduling method based on past meeting data. The meeting scheduling unit refers to past meeting data and selects a method to improve meeting efficiency. The meeting scheduling unit analyzes past meeting data and selects a scheduling method that minimizes errors. As a result, meeting scheduling becomes more efficient by selecting the optimal scheduling method based on past meeting data.

[0109] The meeting scheduling unit can estimate the user's emotions and prioritize meetings based on those emotions. For example, if the user is stressed, the meeting scheduling unit will postpone less important meetings. If the user is relaxed, the meeting scheduling unit will prioritize high-priority meetings. If the user is in a hurry, the meeting scheduling unit will immediately schedule the most important meetings. This streamlines meeting scheduling by prioritizing meetings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The meeting scheduling unit can determine meeting priorities based on project submission deadlines when scheduling meetings. For example, the meeting scheduling unit will determine meeting priorities based on project submission deadlines. The meeting scheduling unit will prioritize meetings with upcoming submission deadlines. The meeting scheduling unit will adjust the meeting scheduling method according to the submission deadlines. This streamlines meeting scheduling by determining meeting priorities based on project submission deadlines.

[0111] The email sending unit can estimate the user's emotions and adjust the email sending method based on the estimated emotions. For example, if the user is stressed, the email sending unit will send a concise email. If the user is relaxed, the email sending unit will send a detailed email. If the user is in a hurry, the email sending unit will send an email quickly. This makes email sending more efficient by adjusting the email sending method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The email sending unit can select the optimal sending method by referring to past email data when sending an email. For example, the email sending unit selects the optimal sending method based on past email data. The email sending unit refers to past email data and selects a method to improve email efficiency. The email sending unit analyzes past email data and selects a sending method that minimizes errors. As a result, email sending becomes more efficient by selecting the optimal sending method based on past email data.

[0113] The email sending unit can estimate the user's emotions and prioritize emails based on those emotions. For example, if the user is stressed, the email sending unit will postpone sending less important emails. If the user is relaxed, the email sending unit will prioritize sending more important emails. If the user is in a hurry, the email sending unit will immediately send the most important emails. This makes email sending more efficient by prioritizing emails according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The email sending unit can prioritize emails based on project submission deadlines. For example, the email sending unit prioritizes emails based on project submission deadlines. The email sending unit sends emails with upcoming submission deadlines first. The email sending unit adjusts the email sending method according to the submission deadline. This streamlines email sending by prioritizing emails based on project submission deadlines.

[0115] The daily report creation system can estimate the user's emotions and adjust the method of creating the daily report based on the estimated emotions. For example, if the user is stressed, the system will create a concise daily report. If the user is relaxed, the system will create a detailed daily report. If the user is in a hurry, the system will create a daily report quickly. This makes daily report creation more efficient by adjusting the method of creation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The daily report creation department can select the optimal creation method by referring to past daily report data when creating daily reports. For example, the daily report creation department selects the optimal creation method based on past daily report data. The daily report creation department refers to past daily report data and selects a method to improve the efficiency of daily reports. The daily report creation department analyzes past daily report data and selects a creation method that minimizes errors. As a result, daily report creation becomes more efficient by selecting the optimal creation method based on past daily report data.

[0117] The daily report creation system can estimate the user's emotions and prioritize daily reports based on those emotions. For example, if the user is stressed, the system will postpone less important reports. If the user is relaxed, the system will prioritize creating more important reports. If the user is in a hurry, the system will immediately create the most important reports. This streamlines daily report creation by prioritizing reports according to the user's emotions. Emotion estimation can be achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The daily report creation department can prioritize daily reports based on project submission deadlines. For example, the department prioritizes daily reports based on project submission deadlines. The department prioritizes creating daily reports with upcoming submission deadlines. The department adjusts the daily report creation method according to the submission deadline. This streamlines daily report creation by prioritizing daily reports based on project submission deadlines.

[0119] The risk analysis unit can estimate the user's emotions and adjust the risk analysis method based on the estimated emotions. For example, if the user is stressed, the risk analysis unit performs a concise risk analysis. If the user is relaxed, the risk analysis unit performs a detailed risk analysis. If the user is in a hurry, the risk analysis unit performs a rapid risk analysis. This makes risk analysis more efficient by adjusting the risk analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The Risk Analysis Department can select the optimal analysis method by referring to past risk data during risk analysis. For example, the Risk Analysis Department selects the optimal analysis method based on past risk data. The Risk Analysis Department refers to past risk data and determines the priority of risks. The Risk Analysis Department analyzes past risk data and proposes an efficient analysis method. In this way, risk analysis becomes more efficient by selecting the optimal analysis method based on past risk data.

[0121] The Risk Analysis Department can apply different analytical methods to each project category during risk analysis. For example, the Risk Analysis Department applies the most suitable analytical method according to the project category. The Risk Analysis Department determines the priority of risks for each project category. The Risk Analysis Department selects an analytical method based on the project category. This makes risk analysis more efficient by applying the most suitable analytical method according to the project category.

[0122] The risk analysis unit can estimate the user's emotions and prioritize risks based on those emotions. For example, if the user is stressed, the risk analysis unit will postpone lower-priority risks. If the user is relaxed, the risk analysis unit will prioritize analyzing higher-priority risks. If the user is in a hurry, the risk analysis unit will immediately analyze the most important risks. This streamlines risk analysis by prioritizing risks according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0123] The risk analysis department can prioritize risks based on the project submission deadline during risk analysis. For example, the risk analysis department prioritizes risks based on the project submission deadline. The risk analysis department prioritizes analyzing risks with upcoming submission deadlines. The risk analysis department adjusts its risk analysis methods according to the submission deadline. This streamlines risk analysis by prioritizing risks based on the project submission deadline.

[0124] The risk analysis department can improve the accuracy of its risk analysis by referring to relevant project literature. For example, the risk analysis department can improve the accuracy of its risk analysis by referring to relevant project literature. Based on the relevant literature, the risk analysis department can determine the priority of risks. The risk analysis department can propose efficient risk analysis methods by referring to relevant literature. Thus, by referring to relevant project literature, the accuracy of the risk analysis is improved.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] AI project management systems can also include features to evaluate the individual work performance of project members in order to understand project progress. For example, the monitoring unit can monitor the speed and quality of each member's task completion and collect performance data. The management unit can then reallocate tasks and optimize resources based on the collected performance data. Furthermore, the execution unit can use the performance data to automatically assign tasks according to the skills of the members. This allows for a more accurate understanding of project progress and more efficient task management.

[0127] The AI ​​project management system can also estimate the emotions of project members to understand project progress and adjust the way progress is reported based on those estimated emotions. For example, if a member is stressed, the system will provide a concise report; if a member is relaxed, a detailed report; and if a member is in a hurry, a to-the-point report. This improves the efficiency of reporting by adjusting the progress reporting method according to the emotions of the members.

[0128] AI project management systems can also provide dashboards that visualize project progress to help users understand the project's status. For example, the monitoring unit can display progress in real time as graphs and charts. The management unit can adjust task priorities and resource allocations through the dashboard. The execution unit can also use the dashboard to monitor task progress and reallocate tasks as needed. This makes it easier to visually understand project progress and enables efficient project management.

[0129] AI project management systems can also include features to analyze the communication history of project members in order to understand the progress of a project. For example, the monitoring unit can analyze the content of emails and chats between members to understand the frequency and content of communication. The management unit can identify the progress and problems of tasks based on the communication history and take necessary measures. Furthermore, the execution unit can use the communication history to strengthen collaboration among members and ensure the smooth execution of tasks. This allows for a more detailed understanding of project progress and enables more efficient project management.

[0130] AI project management systems can also include feedback functions to improve the productivity of project members in order to understand project progress. For example, the monitoring unit can monitor the speed and quality of members' task completion and provide feedback. The management unit can then reallocate tasks and optimize resources based on the feedback. Furthermore, the execution unit can use the feedback to automatically assign tasks according to members' skills. This allows for a more accurate understanding of project progress and more efficient task management.

[0131] AI project management systems can also estimate the emotions of project members to understand project progress and prioritize tasks based on those emotions. For example, if a member is stressed, the management team will postpone less important tasks. If a member is relaxed, they will prioritize high-priority tasks. If a member is in a hurry, they will immediately manage the most important tasks. This streamlines task management by prioritizing tasks according to the emotions of the members.

[0132] The AI ​​project management system can also estimate the emotions of project members to understand project progress and adjust task execution based on those emotions. For example, if a member is stressed, the execution team will start with easy tasks. If a member is relaxed, they will tackle more complex tasks. If a member is in a hurry, tasks that can be completed quickly will be prioritized. This streamlines task execution by adjusting the execution method according to the members' emotions.

[0133] The AI ​​project management system can also estimate the emotions of project members to understand project progress and adjust how meetings are scheduled based on those estimates. For example, if a member is stressed, the meeting scheduling function will schedule a short, efficient meeting. If a member is relaxed, it will schedule a meeting with a detailed agenda. If a member is in a hurry, it will schedule a meeting quickly. This streamlines meeting scheduling by adjusting how meetings are scheduled according to the members' emotions.

[0134] The AI ​​project management system can also estimate the emotions of project members to understand project progress and adjust email sending methods based on those estimates. For example, the email sending function might send a concise email if a member is stressed, a detailed email if they are relaxed, and a quick email if they are in a hurry. This streamlines email sending by adjusting the method of sending emails according to the members' emotions.

[0135] The AI ​​project management system can also estimate the emotions of project members to understand project progress and adjust how daily reports are created based on those estimated emotions. For example, the daily report creation team might create a concise report if a member is stressed, a detailed report if a member is relaxed, and a quick report if a member is in a hurry. This streamlines the daily report creation process by adjusting the method of creation according to the members' emotions.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The reception desk accepts project information. This project information includes the project name, start date, end date, assigned person, and progress status. The reception desk provides an interface for entering this information. Step 2: The monitoring unit grasps the project's progress based on the information received by the reception unit. This progress includes task completion rates, deadline adherence, and resource usage. The monitoring unit can monitor project progress in real time and display the progress as graphs or charts. Step 3: The management department manages tasks based on the progress tracked by the tracking department. Tasks include task type, priority, assignee, and deadline. The management department determines task priorities and allocates resources. They can also track task progress and reallocate tasks as needed. Step 4: The execution unit executes tasks managed by the management unit. The execution unit provides the task execution procedures and monitors the task execution status. It can also record the task execution results and use them to improve subsequent tasks.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0140] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the reception unit, information gathering unit, management unit, execution unit, meeting scheduling unit, email sending unit, daily report creation unit, and risk analysis unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input of project information. The information gathering unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and grasps the progress of the project. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages tasks. The execution unit is implemented by, for example, the control unit 46A of the smart device 14 and executes tasks. The meeting scheduling unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically schedules meetings. The email sending unit is implemented by, for example, the control unit 46A of the smart device 14 and automatically sends emails related to the project. The daily report creation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically creates a daily report on the project's progress. The risk analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically analyzes the project's risks and proposes countermeasures. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0146] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the reception unit, information gathering unit, management unit, execution unit, meeting scheduling unit, email sending unit, daily report creation unit, and risk analysis unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input of project information. The information gathering unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and grasps the progress of the project. The management unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and manages tasks. The execution unit is implemented by, for example, the control unit 46A of the smart glasses 214 and executes tasks. The meeting scheduling unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automatically schedules meetings. The email sending unit is implemented by, for example, the control unit 46A of the smart glasses 214 and automatically sends emails related to the project. The daily report creation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically creates a daily report on the project's progress. The risk analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically analyzes the project's risks and proposes countermeasures. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0162] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the reception unit, information gathering unit, management unit, execution unit, meeting scheduling unit, email sending unit, daily report creation unit, and risk analysis unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input of project information. The information gathering unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and grasps the progress of the project. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages tasks. The execution unit is implemented by, for example, the control unit 46A of the headset terminal 314 and executes tasks. The meeting scheduling unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically sets up meetings. The email sending unit is implemented by, for example, the control unit 46A of the headset terminal 314 and automatically sends emails related to the project. The daily report creation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically creates a daily report on the project's progress. The risk analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically analyzes the project's risks and proposes countermeasures. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0176] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0177] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0178] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0179] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0180] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0181] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0182] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0183] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0184] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0185] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0186] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0187] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0188] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0189] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0190] Each of the multiple elements described above, including the reception unit, information gathering unit, management unit, execution unit, meeting scheduling unit, email sending unit, daily report creation unit, and risk analysis unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input of project information. The information gathering unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and grasps the progress of the project. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages tasks. The execution unit is implemented by, for example, the control unit 46A of the robot 414 and executes tasks. The meeting scheduling unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically sets up meetings. The email sending unit is implemented by, for example, the control unit 46A of the robot 414 and automatically sends emails related to the project. The daily report creation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically creates a daily report on the project's progress. The risk analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and automatically analyzes the project's risks and proposes countermeasures. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0191] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0192] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0193] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0194] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0195] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0196] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0197] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0198] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0199] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0200] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0201] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0202] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0203] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0204] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0205] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0206] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0207] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0208] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0209] (Note 1) A reception desk that accepts project information input, A tracking unit that grasps the progress of the project based on the information received by the aforementioned reception unit, A management unit manages tasks based on the progress status grasped by the aforementioned grasping unit, The system comprises an execution unit that performs tasks managed by the aforementioned management unit. A system characterized by the following features. (Note 2) Equipped with a meeting setting unit that automatically sets up meetings. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes an email sending section that automatically sends project-related emails. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a daily report creation unit that automatically generates daily reports on the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 5) The project includes a risk analysis department that automatically analyzes project risks and proposes countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, Prioritize tasks and allocate resources. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of project information input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze past project information and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering project information, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of project information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering project information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering project information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The gripping part is, We estimate the user's emotions and adjust how we track progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The gripping part is, When monitoring progress, we refer to past project data to predict current progress. The system described in Appendix 1, characterized by the features described herein. (Note 15) The gripping part is, When monitoring progress, different monitoring methods are applied to each project category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The gripping part is, It estimates the user's emotions and adjusts how progress is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The gripping part is, When monitoring progress, prioritize projects based on their submission deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 18) The gripping part is, When monitoring progress, refer to relevant project documentation to improve the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, It estimates the user's emotions and adjusts how tasks are managed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, When managing tasks, refer to past task data to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, When managing tasks, apply different management methods to each project category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, It estimates the user's emotions and determines task priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, When managing tasks, prioritize tasks based on project submission deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, When managing tasks, refer to relevant project literature to improve the accuracy of management. The system described in Appendix 1, characterized by the features described herein. (Note 25) The execution unit is, It estimates the user's emotions and adjusts how tasks are performed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The execution unit is, When executing a task, the system selects the optimal execution method by referring to past execution data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The execution unit is, When executing tasks, different execution methods are applied depending on the project category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The execution unit is, It estimates the user's emotions and determines the order in which tasks should be executed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The execution unit is, When executing tasks, prioritize them based on the project's submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 30) The execution unit is, When executing tasks, refer to relevant project literature to improve execution accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned meeting scheduling unit, It estimates the user's emotions and adjusts how the meeting is set up based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned meeting scheduling unit, When setting up a meeting, the system will refer to past meeting data to select the optimal setup method. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned meeting scheduling unit, It estimates user emotions and determines meeting priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned meeting scheduling unit, When scheduling meetings, prioritize them based on project submission deadlines. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned email sending unit is: It estimates the user's emotions and adjusts how emails are sent based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned email sending unit is: When sending an email, the system refers to past email data to select the most suitable sending method. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned email sending unit is: It estimates the user's emotions and prioritizes emails based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned email sending unit is: When sending emails, prioritize them based on the project submission deadline. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned daily report creation department, The system estimates the user's emotions and adjusts the daily report creation method based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned daily report creation department, When creating a daily report, refer to past daily report data to select the most suitable method of creation. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned daily report creation department, The system estimates user sentiment and prioritizes daily reports based on the estimated sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned daily report creation department, When creating daily reports, prioritize them based on the project's submission deadline. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned risk analysis unit, We estimate user sentiment and adjust risk analysis methods based on the estimated user sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 44) The aforementioned risk analysis unit, When conducting risk analysis, the optimal analysis method is selected by referring to past risk data. The system described in Appendix 5, characterized by the features described herein. (Note 45) The aforementioned risk analysis unit, When conducting risk analysis, different analytical methods are applied to each project category. The system described in Appendix 5, characterized by the features described herein. (Note 46) The aforementioned risk analysis unit, It estimates user sentiment and prioritizes risks based on the estimated user sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 47) The aforementioned risk analysis unit, When conducting a risk analysis, prioritize risks based on the project submission deadline. The system described in Appendix 5, characterized by the features described herein. (Note 48) The aforementioned risk analysis unit, When conducting risk analysis, referencing relevant project literature improves the accuracy of the analysis. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0210] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts project information input, A tracking unit that grasps the progress of the project based on the information received by the aforementioned reception unit, A management unit manages tasks based on the progress status grasped by the aforementioned grasping unit, The system comprises an execution unit that performs tasks managed by the aforementioned management unit. A system characterized by the following features.

2. Equipped with a meeting setting unit that automatically sets up meetings. The system according to feature 1.

3. It includes an email sending section that automatically sends project-related emails. The system according to feature 1.

4. It includes a daily report creation unit that automatically generates daily reports on the project's progress. The system according to feature 1.

5. The project includes a risk analysis department that automatically analyzes project risks and proposes countermeasures. The system according to feature 1.

6. The aforementioned management department, Prioritize tasks and allocate resources. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of project information input based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze past project information and select the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When entering project information, filtering is performed based on the user's current work situation and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of project information to be entered based on the estimated user emotions. The system according to feature 1.

11. The aforementioned reception unit is When entering project information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system according to feature 1.

12. The aforementioned reception unit is When entering project information, the system analyzes the user's social media activity and inputs relevant information. The system according to feature 1.

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